SYSTEM AND METHOD FOR SUPPORTING WIRE EFFICIENT REPLICATION USING SELF-DESCRIPTIVE DATA BUFFER

A method, computer program product, and computer system for receiving, by a computing device, an IO request. It may be determined that data of the IO request includes an indication of an attribute. A prefix descriptor may be constructed for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute. An appropriate Application Programming Interface (API) may be called to process the IO request based upon, at least in part, the prefix descriptor.

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Description
BACKGROUND

The next generation of storage arrays (e.g., midrange storage arrays) may support a rich set of data efficiency features, including deduplication, compression, pattern detection, etc. With the data efficiency features, data may be stored in persistent media in the most efficient form. When replicating or migrating the data, it may be beneficial to preserve the data in its efficient form, to save network bandwidth, to speed up replication data transfer, and to potentially save precious storage system CPU and memory resources from duplicating data efficiency effort at a replication target.

BRIEF SUMMARY OF DISCLOSURE

In one example implementation, a method, performed by one or more computing devices, may include but is not limited to receiving, by a computing device, an IO request. It may be determined that data of the IO request includes an indication of an attribute. A prefix descriptor may be constructed for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute. An appropriate Application Programming Interface (API) may be called to process the IO request based upon, at least in part, the prefix descriptor.

One or more of the following example features may be included. The appropriate API may be a write compressed API when the prefix descriptor indicates compressed data. A bitmap of the self-descriptive page buffer and length of the self-descriptive page buffer may be checked. The prefix descriptor may be added to the data in the self-descriptive page buffer after compression of the data. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is a known pattern. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is deduplicated. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is compressed.

In another example implementation, a computing system may include one or more processors and one or more memories configured to perform operations that may include but are not limited to receiving an IO request. It may be determined that data of the IO request includes an indication of an attribute. A prefix descriptor may be constructed for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute. An appropriate Application Programming Interface (API) may be called to process the IO request based upon, at least in part, the prefix descriptor.

One or more of the following example features may be included. The appropriate API may be a write compressed API when the prefix descriptor indicates compressed data. A bitmap of the self-descriptive page buffer and length of the self-descriptive page buffer may be checked. The prefix descriptor may be added to the data in the self-descriptive page buffer after compression of the data. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is a known pattern. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is deduplicated. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is compressed.

In another example implementation, a computer program product may reside on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, may cause at least a portion of the one or more processors to perform operations that may include but are not limited to receiving an IO request. It may be determined that data of the IO request includes an indication of an attribute. A prefix descriptor may be constructed for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute. An appropriate Application Programming Interface (API) may be called to process the IO request based upon, at least in part, the prefix descriptor.

One or more of the following example features may be included. The appropriate API may be a write compressed API when the prefix descriptor indicates compressed data. A bitmap of the self-descriptive page buffer and length of the self-descriptive page buffer may be checked. The prefix descriptor may be added to the data in the self-descriptive page buffer after compression of the data. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is a known pattern. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is deduplicated. The prefix descriptor may include a type of the attribute, wherein the attribute may include an indication whether the data is compressed.

The details of one or more example implementations are set forth in the accompanying drawings and the description below. Other possible example features and/or possible example advantages will become apparent from the description, the drawings, and the claims. Some implementations may not have those possible example features and/or possible example advantages, and such possible example features and/or possible example advantages may not necessarily be required of some implementations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example diagrammatic view of a IO process coupled to an example distributed computing network according to one or more example implementations of the disclosure;

FIG. 2 is an example diagrammatic view of a storage system of FIG. 1 according to one or more example implementations of the disclosure;

FIG. 3 is an example diagrammatic view of a storage target of FIG. 1 according to one or more example implementations of the disclosure; and

FIG. 4 is an example flowchart of a IO process according to one or more example implementations of the disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION System Overview

In some implementations, the present disclosure may be embodied as a method, system, or computer program product. Accordingly, in some implementations, the present disclosure may take the form of an entirely hardware implementation, an entirely software implementation (including firmware, resident software, micro-code, etc.) or an implementation combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, in some implementations, the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium.

In some implementations, any suitable computer usable or computer readable medium (or media) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer-usable, or computer-readable, storage medium (including a storage device associated with a computing device or client electronic device) may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a digital versatile disk (DVD), a static random access memory (SRAM), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, a media such as those supporting the internet or an intranet, or a magnetic storage device. Note that the computer-usable or computer-readable medium could even be a suitable medium upon which the program is stored, scanned, compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. In the context of the present disclosure, a computer-usable or computer-readable, storage medium may be any tangible medium that can contain or store a program for use by or in connection with the instruction execution system, apparatus, or device.

In some implementations, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. In some implementations, such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. In some implementations, the computer readable program code may be transmitted using any appropriate medium, including but not limited to the internet, wireline, optical fiber cable, RF, etc. In some implementations, a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

In some implementations, computer program code for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java®, Smalltalk, C++ or the like. Java® and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. However, the computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages, such as the “C” programming language, PASCAL, or similar programming languages, as well as in scripting languages such as Javascript, PERL, or Python. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the internet using an Internet Service Provider). In some implementations, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs) or other hardware accelerators, micro-controller units (MCUs), or programmable logic arrays (PLAs) may execute the computer readable program instructions/code by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

In some implementations, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus (systems), methods and computer program products according to various implementations of the present disclosure. Each block in the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams, may represent a module, segment, or portion of code, which comprises one or more executable computer program instructions for implementing the specified logical function(s)/act(s). These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, which may execute via the processor of the computer or other programmable data processing apparatus, create the ability to implement one or more of the functions/acts specified in the flowchart and/or block diagram block or blocks or combinations thereof. It should be noted that, in some implementations, the functions noted in the block(s) may occur out of the order noted in the figures (or combined or omitted). For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

In some implementations, these computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks or combinations thereof.

In some implementations, the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed (not necessarily in a particular order) on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts (not necessarily in a particular order) specified in the flowchart and/or block diagram block or blocks or combinations thereof.

Referring now to the example implementation of FIG. 1, there is shown IO process 10 that may reside on and may be executed by a computer (e.g., computer 12), which may be connected to a network (e.g., network 14) (e.g., the internet or a local area network). Examples of computer 12 (and/or one or more of the client electronic devices noted below) may include, but are not limited to, a storage system (e.g., a Network Attached Storage (NAS) system, a Storage Area Network (SAN)), a personal computer(s), a laptop computer(s), mobile computing device(s), a server computer, a series of server computers, a mainframe computer(s), or a computing cloud(s). As is known in the art, a SAN may include one or more of the client electronic devices, including a RAID device and a NAS system. In some implementations, each of the aforementioned may be generally described as a computing device. In certain implementations, a computing device may be a physical or virtual device. In many implementations, a computing device may be any device capable of performing operations, such as a dedicated processor, a portion of a processor, a virtual processor, a portion of a virtual processor, portion of a virtual device, or a virtual device. In some implementations, a processor may be a physical processor or a virtual processor. In some implementations, a virtual processor may correspond to one or more parts of one or more physical processors. In some implementations, the instructions/logic may be distributed and executed across one or more processors, virtual or physical, to execute the instructions/logic. Computer 12 may execute an operating system, for example, but not limited to, Microsoft® Windows®; Mac® OS X®; Red Hat® Linux®, Windows® Mobile, Chrome OS, Blackberry OS, Fire OS, or a custom operating system. (Microsoft and Windows are registered trademarks of Microsoft Corporation in the United States, other countries or both; Mac and OS X are registered trademarks of Apple Inc. in the United States, other countries or both; Red Hat is a registered trademark of Red Hat Corporation in the United States, other countries or both; and Linux is a registered trademark of Linus Torvalds in the United States, other countries or both).

In some implementations, as will be discussed below in greater detail, a IO process, such as IO process 10 of FIG. 1, may receive, by a computing device, an IO request. It may be determined that data of the IO request includes an indication of an attribute. A prefix descriptor may be constructed for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute. An appropriate Application Programming Interface (API) may be called to process the IO request based upon, at least in part, the prefix descriptor.

In some implementations, the instruction sets and subroutines of IO process 10, which may be stored on storage device, such as storage device 16, coupled to computer 12, may be executed by one or more processors and one or more memory architectures included within computer 12. In some implementations, storage device 16 may include but is not limited to: a hard disk drive; all forms of flash memory storage devices; a tape drive; an optical drive; a RAID array (or other array); a random access memory (RAM); a read-only memory (ROM); or combination thereof. In some implementations, storage device 16 may be organized as an extent, an extent pool, a RAID extent (e.g., an example 4D+1P R5, where the RAID extent may include, e.g., five storage device extents that may be allocated from, e.g., five different storage devices), a mapped RAID (e.g., a collection of RAID extents), or combination thereof.

In some implementations, network 14 may be connected to one or more secondary networks (e.g., network 18), examples of which may include but are not limited to: a local area network; a wide area network or other telecommunications network facility; or an intranet, for example. The phrase “telecommunications network facility,” as used herein, may refer to a facility configured to transmit, and/or receive transmissions to/from one or more mobile client electronic devices (e.g., cellphones, etc.) as well as many others.

In some implementations, computer 12 may include a data store, such as a database (e.g., relational database, object-oriented database, triplestore database, etc.) and may be located within any suitable memory location, such as storage device 16 coupled to computer 12. In some implementations, data, metadata, information, etc. described throughout the present disclosure may be stored in the data store. In some implementations, computer 12 may utilize any known database management system such as, but not limited to, DB2, in order to provide multi-user access to one or more databases, such as the above noted relational database. In some implementations, the data store may also be a custom database, such as, for example, a flat file database or an XML database. In some implementations, any other form(s) of a data storage structure and/or organization may also be used. In some implementations, IO process 10 may be a component of the data store, a standalone application that interfaces with the above noted data store and/or an applet/application that is accessed via client applications 22, 24, 26, 28. In some implementations, the above noted data store may be, in whole or in part, distributed in a cloud computing topology. In this way, computer 12 and storage device 16 may refer to multiple devices, which may also be distributed throughout the network.

In some implementations, computer 12 may execute a storage management application (e.g., storage management application 21), examples of which may include, but are not limited to, e.g., a storage system application, a cloud computing application, a data synchronization application, a data migration application, a garbage collection application, or other application that allows for the implementation and/or management of data in a clustered (or non-clustered) environment (or the like). In some implementations, IO process 10 and/or storage management application 21 may be accessed via one or more of client applications 22, 24, 26, 28. In some implementations, IO process 10 may be a standalone application, or may be an applet/application/script/extension that may interact with and/or be executed within storage management application 21, a component of storage management application 21, and/or one or more of client applications 22, 24, 26, 28. In some implementations, storage management application 21 may be a standalone application, or may be an applet/application/script/extension that may interact with and/or be executed within IO process 10, a component of IO process 10, and/or one or more of client applications 22, 24, 26, 28. In some implementations, one or more of client applications 22, 24, 26, 28 may be a standalone application, or may be an applet/application/script/extension that may interact with and/or be executed within and/or be a component of IO process 10 and/or storage management application 21. Examples of client applications 22, 24, 26, 28 may include, but are not limited to, e.g., a storage system application, a cloud computing application, a data synchronization application, a data migration application, a garbage collection application, or other application that allows for the implementation and/or management of data in a clustered (or non-clustered) environment (or the like), a standard and/or mobile web browser, an email application (e.g., an email client application), a textual and/or a graphical user interface, a customized web browser, a plugin, an Application Programming Interface (API), or a custom application. The instruction sets and subroutines of client applications 22, 24, 26, 28, which may be stored on storage devices 30, 32, 34, 36, coupled to client electronic devices 38, 40, 42, 44, may be executed by one or more processors and one or more memory architectures incorporated into client electronic devices 38, 40, 42, 44.

In some implementations, one or more of storage devices 30, 32, 34, 36, may include but are not limited to: hard disk drives; flash drives, tape drives; optical drives; RAID arrays; random access memories (RAM); and read-only memories (ROM). Examples of client electronic devices 38, 40, 42, 44 (and/or computer 12) may include, but are not limited to, a personal computer (e.g., client electronic device 38), a laptop computer (e.g., client electronic device 40), a smart/data-enabled, cellular phone (e.g., client electronic device 42), a notebook computer (e.g., client electronic device 44), a tablet, a server, a television, a smart television, a smart speaker, an Internet of Things (IoT) device, a media (e.g., video, photo, etc.) capturing device, and a dedicated network device. Client electronic devices 38, 40, 42, 44 may each execute an operating system, examples of which may include but are not limited to, Android™, Apple® iOS®, Mac® OS X®; Red Hat® Linux®, Windows® Mobile, Chrome OS, Blackberry OS, Fire OS, or a custom operating system.

In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of IO process 10 (and vice versa). Accordingly, in some implementations, IO process 10 may be a purely server-side application, a purely client-side application, or a hybrid server-side/client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or IO process 10.

In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of storage management application 21 (and vice versa). Accordingly, in some implementations, storage management application 21 may be a purely server-side application, a purely client-side application, or a hybrid server-side/client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or storage management application 21. As one or more of client applications 22, 24, 26, 28, IO process 10, and storage management application 21, taken singly or in any combination, may effectuate some or all of the same functionality, any description of effectuating such functionality via one or more of client applications 22, 24, 26, 28, IO process 10, storage management application 21, or combination thereof, and any described interaction(s) between one or more of client applications 22, 24, 26, 28, IO process 10, storage management application 21, or combination thereof to effectuate such functionality, should be taken as an example only and not to limit the scope of the disclosure.

In some implementations, one or more of users 46, 48, 50, 52 may access computer 12 and IO process 10 (e.g., using one or more of client electronic devices 38, 40, 42, 44) directly through network 14 or through secondary network 18. Further, computer 12 may be connected to network 14 through secondary network 18, as illustrated with phantom link line 54. IO process 10 may include one or more user interfaces, such as browsers and textual or graphical user interfaces, through which users 46, 48, 50, 52 may access IO process 10.

In some implementations, the various client electronic devices may be directly or indirectly coupled to network 14 (or network 18). For example, client electronic device 38 is shown directly coupled to network 14 via a hardwired network connection. Further, client electronic device 44 is shown directly coupled to network 18 via a hardwired network connection. Client electronic device 40 is shown wirelessly coupled to network 14 via wireless communication channel 56 established between client electronic device 40 and wireless access point (i.e., WAP) 58, which is shown directly coupled to network 14. WAP 58 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, Wi-Fi®, RFID, and/or Bluetooth™ (including Bluetooth™ Low Energy) device that is capable of establishing wireless communication channel 56 between client electronic device 40 and WAP 58. Client electronic device 42 is shown wirelessly coupled to network 14 via wireless communication channel 60 established between client electronic device 42 and cellular network/bridge 62, which is shown by example directly coupled to network 14.

In some implementations, some or all of the IEEE 802.11x specifications may use Ethernet protocol and carrier sense multiple access with collision avoidance (i.e., CSMA/CA) for path sharing. The various 802.11x specifications may use phase-shift keying (i.e., PSK) modulation or complementary code keying (i.e., CCK) modulation, for example. Bluetooth™ (including Bluetooth™ Low Energy) is a telecommunications industry specification that allows, e.g., mobile phones, computers, smart phones, and other electronic devices to be interconnected using a short-range wireless connection. Other forms of interconnection (e.g., Near Field Communication (NFC)) may also be used.

In some implementations, various I/O requests (e.g., I/O request 15) may be sent from, e.g., client applications 22, 24, 26, 28 to, e.g., computer 12. Examples of I/O request 15 may include but are not limited to, data write requests (e.g., a request that content be written to computer 12) and data read requests (e.g., a request that content be read from computer 12).

Data Storage System

Referring also to the example implementation of FIGS. 2-3 (e.g., where computer 12 may be configured as a data storage system), computer 12 may include storage processor 100 and a plurality of storage targets (e.g., storage targets 102, 104, 106, 108, 110). In some implementations, storage targets 102, 104, 106, 108, 110 may include any of the above-noted storage devices. In some implementations, storage targets 102, 104, 106, 108, 110 may be configured to provide various levels of performance and/or high availability. For example, storage targets 102, 104, 106, 108, 110 may be configured to form a non-fully-duplicative fault-tolerant data storage system (such as a non-fully-duplicative RAID data storage system), examples of which may include but are not limited to: RAID 3 arrays, RAID 4 arrays, RAID 5 arrays, and/or RAID 6 arrays. It will be appreciated that various other types of RAID arrays may be used without departing from the scope of the present disclosure.

While in this particular example, computer 12 is shown to include five storage targets (e.g., storage targets 102, 104, 106, 108, 110), this is for example purposes only and is not intended limit the present disclosure. For instance, the actual number of storage targets may be increased or decreased depending upon, e.g., the level of redundancy/performance/capacity required.

Further, the storage targets (e.g., storage targets 102, 104, 106, 108, 110) included with computer 12 may be configured to form a plurality of discrete storage arrays. For instance, and assuming for example purposes only that computer 12 includes, e.g., ten discrete storage targets, a first five targets (of the ten storage targets) may be configured to form a first RAID array and a second five targets (of the ten storage targets) may be configured to form a second RAID array.

In some implementations, one or more of storage targets 102, 104, 106, 108, 110 may be configured to store coded data (e.g., via storage management process 21), wherein such coded data may allow for the regeneration of data lost/corrupted on one or more of storage targets 102, 104, 106, 108, 110. Examples of such coded data may include but is not limited to parity data and Reed-Solomon data. Such coded data may be distributed across all of storage targets 102, 104, 106, 108, 110 or may be stored within a specific storage target.

Examples of storage targets 102, 104, 106, 108, 110 may include one or more data arrays, wherein a combination of storage targets 102, 104, 106, 108, 110 (and any processing/control systems associated with storage management application 21) may form data array 112.

The manner in which computer 12 is implemented may vary depending upon e.g., the level of redundancy/performance/capacity required. For example, computer 12 may be configured as a SAN (i.e., a Storage Area Network), in which storage processor 100 may be, e.g., a dedicated computing system and each of storage targets 102, 104, 106, 108, 110 may be a RAID device. An example of storage processor 100 may include but is not limited to a VPLEX™, VNX™, or Unity™ system offered by Dell EMC™ of Hopkinton, Mass.

In the example where computer 12 is configured as a SAN, the various components of computer 12 (e.g., storage processor 100, and storage targets 102, 104, 106, 108, 110) may be coupled using network infrastructure 114, examples of which may include but are not limited to an Ethernet (e.g., Layer 2 or Layer 3) network, a fiber channel network, an InfiniBand network, or any other circuit switched/packet switched network.

As discussed above, various I/O requests (e.g., I/O request 15) may be generated. For example, these I/O requests may be sent from, e.g., client applications 22, 24, 26, 28 to, e.g., computer 12. Additionally/alternatively (e.g., when storage processor 100 is configured as an application server or otherwise), these I/O requests may be internally generated within storage processor 100 (e.g., via storage management process 21). Examples of I/O request 15 may include but are not limited to data write request 116 (e.g., a request that content 118 be written to computer 12) and data read request 120 (e.g., a request that content 118 be read from computer 12).

In some implementations, during operation of storage processor 100, content 118 to be written to computer 12 may be received and/or processed by storage processor 100 (e.g., via storage management process 21). Additionally/alternatively (e.g., when storage processor 100 is configured as an application server or otherwise), content 118 to be written to computer 12 may be internally generated by storage processor 100 (e.g., via storage management process 21).

As discussed above, the instruction sets and subroutines of storage management application 21, which may be stored on storage device 16 included within computer 12, may be executed by one or more processors and one or more memory architectures included with computer 12. Accordingly, in addition to being executed on storage processor 100, some or all of the instruction sets and subroutines of storage management application 21 (and/or IO process 10) may be executed by one or more processors and one or more memory architectures included with data array 112.

In some implementations, storage processor 100 may include front end cache memory system 122. Examples of front end cache memory system 122 may include but are not limited to a volatile, solid-state, cache memory system (e.g., a dynamic RAM cache memory system), a non-volatile, solid-state, cache memory system (e.g., a flash-based, cache memory system), and/or any of the above-noted storage devices.

In some implementations, storage processor 100 may initially store content 118 within front end cache memory system 122. Depending upon the manner in which front end cache memory system 122 is configured, storage processor 100 (e.g., via storage management process 21) may immediately write content 118 to data array 112 (e.g., if front end cache memory system 122 is configured as a write-through cache) or may subsequently write content 118 to data array 112 (e.g., if front end cache memory system 122 is configured as a write-back cache).

In some implementations, one or more of storage targets 102, 104, 106, 108, 110 may include a backend cache memory system. Examples of the backend cache memory system may include but are not limited to a volatile, solid-state, cache memory system (e.g., a dynamic RAM cache memory system), a non-volatile, solid-state, cache memory system (e.g., a flash-based, cache memory system), and/or any of the above-noted storage devices.

Storage Targets

As discussed above, one or more of storage targets 102, 104, 106, 108, 110 may be a RAID device. For instance, and referring also to FIG. 3, there is shown example target 150, wherein target 150 may be one example implementation of a RAID implementation of, e.g., storage target 102, storage target 104, storage target 106, storage target 108, and/or storage target 110. An example of target 150 may include but is not limited to a VPLEX™, VNX™, or Unity™ system offered by Dell EMC™ of Hopkinton, Mass. Examples of storage devices 154, 156, 158, 160, 162 may include one or more electro-mechanical hard disk drives, one or more solid-state/flash devices, and/or any of the above-noted storage devices. It will be appreciated that while the term “disk” or “drive” may be used throughout, these may refer to and be used interchangeably with any types of appropriate storage devices as the context and functionality of the storage device permits.

In some implementations, target 150 may include storage processor 152 and a plurality of storage devices (e.g., storage devices 154, 156, 158, 160, 162). Storage devices 154, 156, 158, 160, 162 may be configured to provide various levels of performance and/or high availability (e.g., via storage management process 21). For example, one or more of storage devices 154, 156, 158, 160, 162 (or any of the above-noted storage devices) may be configured as a RAID 0 array, in which data is striped across storage devices. By striping data across a plurality of storage devices, improved performance may be realized. However, RAID 0 arrays may not provide a level of high availability. Accordingly, one or more of storage devices 154, 156, 158, 160, 162 (or any of the above-noted storage devices) may be configured as a RAID 1 array, in which data is mirrored between storage devices. By mirroring data between storage devices, a level of high availability may be achieved as multiple copies of the data may be stored within storage devices 154, 156, 158, 160, 162.

While storage devices 154, 156, 158, 160, 162 are discussed above as being configured in a RAID 0 or RAID 1 array, this is for example purposes only and not intended to limit the present disclosure, as other configurations are possible. For example, storage devices 154, 156, 158, 160, 162 may be configured as a RAID 3, RAID 4, RAID 5 or RAID 6 array.

While in this particular example, target 150 is shown to include five storage devices (e.g., storage devices 154, 156, 158, 160, 162), this is for example purposes only and not intended to limit the present disclosure. For instance, the actual number of storage devices may be increased or decreased depending upon, e.g., the level of redundancy/performance/capacity required.

In some implementations, one or more of storage devices 154, 156, 158, 160, 162 may be configured to store (e.g., via storage management process 21) coded data, wherein such coded data may allow for the regeneration of data lost/corrupted on one or more of storage devices 154, 156, 158, 160, 162. Examples of such coded data may include but are not limited to parity data and Reed-Solomon data. Such coded data may be distributed across all of storage devices 154, 156, 158, 160, 162 or may be stored within a specific storage device.

The manner in which target 150 is implemented may vary depending upon e.g., the level of redundancy/performance/capacity required. For example, target 150 may be a RAID device in which storage processor 152 is a RAID controller card and storage devices 154, 156, 158, 160, 162 are individual “hot-swappable” hard disk drives. Another example of target 150 may be a RAID system, examples of which may include but are not limited to an NAS (i.e., Network Attached Storage) device or a SAN (i.e., Storage Area Network).

In some implementations, storage target 150 may execute all or a portion of storage management application 21. The instruction sets and subroutines of storage management application 21, which may be stored on a storage device (e.g., storage device 164) coupled to storage processor 152, may be executed by one or more processors and one or more memory architectures included with storage processor 152. Storage device 164 may include but is not limited to any of the above-noted storage devices.

As discussed above, computer 12 may be configured as a SAN, wherein storage processor 100 may be a dedicated computing system and each of storage targets 102, 104, 106, 108, 110 may be a RAID device. Accordingly, when storage processor 100 processes data requests 116, 120, storage processor 100 (e.g., via storage management process 21) may provide the appropriate requests/content (e.g., write request 166, content 168 and read request 170) to, e.g., storage target 150 (which is representative of storage targets 102, 104, 106, 108 and/or 110).

In some implementations, during operation of storage processor 152, content 168 to be written to target 150 may be processed by storage processor 152 (e.g., via storage management process 21). Storage processor 152 may include cache memory system 172. Examples of cache memory system 172 may include but are not limited to a volatile, solid-state, cache memory system (e.g., a dynamic RAM cache memory system) and/or a non-volatile, solid-state, cache memory system (e.g., a flash-based, cache memory system). During operation of storage processor 152, content 168 to be written to target 150 may be received by storage processor 152 (e.g., via storage management process 21) and initially stored (e.g., via storage management process 21) within front end cache memory system 172.

As noted above, the next generation of storage arrays (e.g., midrange storage arrays) may support a rich set of data efficiency features, including deduplication, compression, pattern detection, etc. With the data efficiency features, data may be stored in persistent media in the most efficient form. When replicating or migrating the data, it may be beneficial to preserve the data in its efficient form, to save network bandwidth, to speed up replication data transfer, and to potentially save precious storage system CPU and memory resources from duplicating data efficiency effort at a replication target.

Given that the data is generally not decompressed and/or re-hydrated locally, context information may need to be saved and provided to ensure later decompression success, and to perform end to end data verification. Also, since there may be multiple methods to store data efficiently, the context information which may need to be saved/provided may vary significantly. To solve the problem, some existing designs may first introduce several new APIs to retrieve deduplication metadata of deduped data blocks, and then retrieve metadata of the compressed data block, and finally read the compressed data and the uncompressed data accordingly. This solution may work, e.g., if deduplication and compression are only performed when data is written to disk; however, it may cause missed reads, e.g., if the system performs post processing deduplication and compression. The latency performance may also be undesirable due to the multiple serialized API calls required. As such, as well be discussed below, the present disclosure enables self-descriptive data blocks for performing wire efficient IO in a single API. Generally, “wire efficient” may mean to use the most bandwidth efficient way to transfer data. For example, compress data first before transferring to a remote site. If the data block itself is self-descriptive, then there is no need to add additional APIs (as is currently done) to retrieve deduplication metadata, or compression metadata, or pattern metadata separately. Moreover, if the data blocks itself is self-descriptive, the associated complexity to connect metadata with data and to ensure consistency between them may be reduced.

The IO Process

As discussed above and referring also at least to the example implementations of FIG. 4, IO process 10 may receive 400, by a computing device, an IO request. IO process 10 may determine 402 that data of the IO request includes an indication of an attribute. IO process 10 may construct 404 a prefix descriptor for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute. IO process 10 may call 406 an appropriate Application Programming Interface (API) to process the IO request based upon, at least in part, the prefix descriptor.

As will be discussed below, the present disclosure enables self-descriptive data blocks for performing wire efficient IOs in a single API. If the data blocks itself is self-descriptive, then there is no need to add/call additional APIs (as is currently done) to retrieve deduplication metadata, or compression metadata, or pattern metadata separately. Moreover, if the data block itself is self-descriptive, the associated complexity to connect metadata with data and to ensure consistency between them may be reduced.

In some implementations, IO process 10 may receive 400, by a computing device, an IO request, and may determine 402 that data of the IO request includes an indication of an attribute. For example, upon receiving 400 an IO (e.g., read) request, as the IO request moves down the virtual and physical layers of the storage system, IO process 10 may check to determine 402 whether the requested data includes an indicator that the data, e.g., is a well-known pattern, has been deduplicated, or is compressed. A “well-known” data pattern may generally be described as data that is presented in a repetitive pattern that may be reconstructed by repeating the same pattern. For example, a block of data with all “0”s, or all “1”s, or “101010”, etc. Deduplicated generally means there are two or more blocks of data with the exact the same content, and stored only one instance. Compressed generally means data that is compressed to a smaller size.

In some implementations, IO process 10 may construct 404 a prefix descriptor for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute. For example, after determining 402 that the data of the IO request includes the above-noted indicator of the attribute that indicates whether the data, e.g., is a well-known pattern, has been deduplicated, or is compressed, IO process 10 may construct 404 a prefix descriptor (e.g., a data efficiency prefix descriptor). As will be discussed below, the data efficiency prefix descriptor may include the type of the attribute, which may include an indication whether the data is a known pattern, whether the data is a deduplicated, and/or whether the data is compressed, and/or whether any other type of data efficiency has been applied. In some implementations, the prefix descriptor may be in the data block itself or in the page buffer (or both). Generally, when in both, the data persisted on the storage device is considered “self-descriptive”.

In some implementations, the prefix descriptor may be added to the data in the self-descriptive page buffer after compression of the data. For example, since the API caller does not know beforehand whether a particular data block is deduped/compressed, etc., IO process 10 may have to allocate a data buffer size for the worst case scenario (e.g., no data efficiency applied to the requested data). Thus, for data blocks with data efficiency applied (e.g., deduplication, compression, etc.), as long as the constructed 404 prefix descriptor size, plus the payload (e.g., data) size, is less than the data if the data were uncompressed, the saved buffer space from data efficiency features may be used to add specific metadata (e.g., data efficiency metadata) to the prefix descriptor of the data. For example, the prefix descriptor of the data for the page buffer may include, e.g.,:

“Magic_number”. This may be a unique number to indicate the header definition and/or embedded header version information. In some implementations, whenever there is buffer format change, a new magic number may be used.

“Buffer-len”. This may indicate the buffer size. In other words, the valid data length inside the read buffer.

“Original-data_len”. This may indicate the data length of the original data (prior to any applied data efficiency), which may be used for validation purposes.

“Original-data-checksum”. This may be the checksum of the original page data, which may be used for data verification upon data decompression.

“Wire_efficient_type”. This may indicate whether the data has been deduplicated, compressed, or includes a special well-known pattern.

“Wire-efficient-config”. This may indicate the type specific configuration information, deduplication hash algorithm, or compression algorithm (e.g., SW or QAI etc.), compression level (e.g., low, medium, high), etc.

“Payload”. This may be the data itself. If the data has been deduplicated, it may be the full deduplication hash. If the data has been compressed, it may be the compressed buffer. For the pattern, it may be the pattern signature. It will be appreciated that the names of the metadata are for example purposes only and should not be taken to limit the scope of the present disclosure. Furthermore, differing or additional metadata may also be used.

In some implementations, IO process 10 may check 408 a bitmap of the self-descriptive page buffer and length of the self-descriptive page buffer, and may call 406 an appropriate Application Programming Interface (API) to process the IO request based upon, at least in part, the prefix descriptor, where in some implementations, the appropriate API may be a write compressed API when the prefix descriptor indicates compressed data. For example, with the disclosed self-descriptive data buffer design, the replication service (e.g., via IO process 10) may only need to know which pages are compressed, which pages are not, and the buffer length of the actual data buffer (the above-noted “buffer_len”). For wire efficient data, IO process 10 may send out data according to the actual length (e.g., prefix descriptor size, plus the payload (e.g., data) size). Otherwise, IO process 10 may send out the whole page buffer (e.g., 4K page buffer). At the target where the data is to be stored, again, the replication service (e.g., via IO process 10) may only need to check 408 the bitmap of the wire efficient page and page buffer length, and then call 406 the appropriate API (e.g., the “regular” write API (e.g., if the data has not been compressed) or call 406 the write compressed API (e.g., if the data has been compressed)). In some implementations, the wire efficient details (e.g., the above-noted data efficiency metadata) may be hidden inside the self-descriptive page buffer, and only the read/write API implementation may need to construct and interpret the buffer descriptor.

The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the language “at least one of A, B, and C” (and the like) should be interpreted as covering only A, only B, only C, or any combination of the three, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps (not necessarily in a particular order), operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps (not necessarily in a particular order), operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents (e.g., of all means or step plus function elements) that may be in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications, variations, substitutions, and any combinations thereof will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The implementation(s) were chosen and described in order to explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various implementation(s) with various modifications and/or any combinations of implementation(s) as are suited to the particular use contemplated.

Having thus described the disclosure of the present application in detail and by reference to implementation(s) thereof, it will be apparent that modifications, variations, and any combinations of implementation(s) (including any modifications, variations, substitutions, and combinations thereof) are possible without departing from the scope of the disclosure defined in the appended claims.

Claims

1. A computer-implemented method comprising:

receiving, by a computing device, an IO request;
determining that data of the IO request includes an indication of an attribute;
constructing a prefix descriptor for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute; and
calling an appropriate Application Programming Interface (API) to process the IO request based upon, at least in part, the prefix descriptor.

2. The computer-implemented method of claim 1 wherein the appropriate API is a write compressed API when the prefix descriptor indicates compressed data.

3. The computer-implemented method of claim 1 further comprising checking a bitmap of the self-descriptive page buffer and length of the self-descriptive page buffer.

4. The computer-implemented method of claim 1 wherein the prefix descriptor is added to the data in the self-descriptive page buffer after compression of the data.

5. The computer-implemented method of claim 1 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes an indication whether the data is a known pattern.

6. The computer-implemented method of claim 1 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes an indication whether the data is deduplicated.

7. The computer-implemented method of claim 1 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes an indication whether the data is compressed.

8. A computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising:

receiving an IO request;
determining that data of the IO request includes an indication of an attribute;
constructing a prefix descriptor for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute; and
calling an appropriate Application Programming Interface (API) to process the IO request based upon, at least in part, the prefix descriptor.

9. The computer program product of claim 8 wherein the appropriate API is a write compressed API when the prefix descriptor indicates compressed data.

10. The computer program product of claim 8 wherein the operations further comprise checking a bitmap of the self-descriptive page buffer and length of the self-descriptive page buffer.

11. The computer program product of claim 8 wherein the prefix descriptor is added to the data in the self-descriptive page buffer after compression of the data.

12. The computer program product of claim 8 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes an indication whether the data is a known pattern.

13. The computer program product of claim 8 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes an indication whether the data is deduplicated.

14. The computer program product of claim 8 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes an indication whether the data is compressed.

15. A computing system including one or more processors and one or more memories configured to perform operations comprising:

receiving an IO request;
determining that data of the IO request includes an indication of an attribute;
constructing a prefix descriptor for the data in a self-descriptive page buffer based upon, at least in part, determining that the data of the IO request includes the indication of the attribute; and
calling an appropriate Application Programming Interface (API) to process the IO request based upon, at least in part, the prefix descriptor.

16. The computing system of claim 15 wherein the appropriate API is a write compressed API when the prefix descriptor indicates compressed data.

17. The computing system of claim 15 wherein the operations further comprise checking a bitmap of the self-descriptive page buffer and length of the self-descriptive page buffer.

18. The computing system of claim 15 wherein the prefix descriptor is added to the data in the self-descriptive page buffer after compression of the data.

19. The computing system of claim 15 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes an indication whether the data is a known pattern.

20. The computing system of claim 15 wherein the prefix descriptor includes a type of the attribute, wherein the attribute includes one of an indication whether the data is deduplicated and whether the data is compressed.

Patent History
Publication number: 20210124700
Type: Application
Filed: Oct 24, 2019
Publication Date: Apr 29, 2021
Patent Grant number: 11100018
Inventors: XIANGPING CHEN (Sherborn, MA), Xunce Zhou (Harvard, MA)
Application Number: 16/662,447
Classifications
International Classification: G06F 13/20 (20060101);